






'''simple docstring'''







import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__lowerCAmelCase        =							os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__lowerCAmelCase        =							"    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"




class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         a_						     =			tempfile.mkdtemp()
         os.makedirs(os.path.join(self.transformer_dir   ,	'models/bert/'	)	)
         a_						     =			self.transformer_dir
         shutil.copy(
             os.path.join(UpperCamelCase__   ,	'src/transformers/models/bert/modeling_bert.py'	)   ,	os.path.join(self.transformer_dir   ,	'models/bert/modeling_bert.py'	)   ,	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			'src/transformers'
         shutil.rmtree(self.transformer_dir	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__=None	):






         """simple docstring"""


         a_						     =			comment + f'\nclass {class_name}(nn.Module):\n' + class_code
         if overwrite_result is not None:
                a_						     =			comment + f'\nclass {class_name}(nn.Module):\n' + overwrite_result
         a_						     =			black.Mode(target_versions={black.TargetVersion.PYaa}   ,	line_length=119	)
         a_						     =			black.format_str(UpperCamelCase__   ,	mode=UpperCamelCase__	)
         a_						     =			os.path.join(self.transformer_dir   ,	'new_code.py'	)
         with open(UpperCamelCase__   ,	'w'   ,	newline='\n'	) as f:
                f.write(UpperCamelCase__	)
         if overwrite_result is None:
                self.assertTrue(len(check_copies.is_copy_consistent(UpperCamelCase__	)	) == 0	)
         else:
                check_copies.is_copy_consistent(f.name   ,	overwrite=UpperCamelCase__	)
                with open(UpperCamelCase__   ,	'r'	) as f:
                       self.assertTrue(f.read()   ,	UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'	)
         self.assertEqual(UpperCamelCase__   ,	UpperCamelCase__	)







  def    _a       (			self	):






         """simple docstring"""


         self.check_copy_consistency(
             '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'   ,	'BertLMPredictionHead'   ,	REFERENCE_CODE + '\n'   ,	)

         # With no empty line at the end
         self.check_copy_consistency(
             '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'   ,	'BertLMPredictionHead'   ,	UpperCamelCase__   ,	)

         # Copy consistency with rename
         self.check_copy_consistency(
             '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'   ,	'TestModelLMPredictionHead'   ,	re.sub('Bert'   ,	'TestModel'   ,	UpperCamelCase__	)   ,	)

         # Copy consistency with a really long name
         a_						     =			'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
         self.check_copy_consistency(
             f'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'   ,	f'{long_class_name}LMPredictionHead'   ,	re.sub('Bert'   ,	UpperCamelCase__   ,	UpperCamelCase__	)   ,	)

         # Copy consistency with overwrite
         self.check_copy_consistency(
             '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'   ,	'TestModelLMPredictionHead'   ,	UpperCamelCase__   ,	overwrite_result=re.sub('Bert'   ,	'TestModel'   ,	UpperCamelCase__	)   ,	)







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			check_copies.LOCALIZED_READMES['README_zh-hans.md']

         a_						     =			(
             '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
             ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
             ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
             ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
             ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
             ' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
             ' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
             ' method has been applied to compress GPT2 into'
             ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
             ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
             ' Multilingual BERT into'
             ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
             ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
             ' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
             ' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
             ' Luong, Quoc V. Le, Christopher D. Manning.'
         )
         a_						     =			(
             '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
             ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
             ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
             ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
         )
         a_						     =			(
             '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
             ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
             ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
             ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
             ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
             ' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
             ' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
             ' method has been applied to compress GPT2 into'
             ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
             ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
             ' Multilingual BERT into'
             ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
             ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
             ' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
             ' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
             ' Christopher D. Manning 发布。\n'
         )

         a_     ,	a_						     =			check_copies.convert_to_localized_md(
             UpperCamelCase__   ,	UpperCamelCase__   ,	localized_readme['format_model_list']	)

         self.assertFalse(UpperCamelCase__	)
         self.assertEqual(UpperCamelCase__   ,	UpperCamelCase__	)

         a_     ,	a_						     =			check_copies.convert_to_localized_md(
             UpperCamelCase__   ,	UpperCamelCase__   ,	localized_readme['format_model_list']	)

         # Check whether the number of models is equal to README.md after conversion.
         self.assertTrue(UpperCamelCase__	)

         a_						     =			(
             '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
             ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
             ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
             ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
         )
         a_						     =			(
             '1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
             ' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
             ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
             ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
         )
         a_						     =			(
             '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
             ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
             ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
             ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
         )

         a_     ,	a_						     =			check_copies.convert_to_localized_md(
             UpperCamelCase__   ,	UpperCamelCase__   ,	localized_readme['format_model_list']	)

         # Check if the model link is synchronized.
         self.assertEqual(UpperCamelCase__   ,	UpperCamelCase__	)












'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class   __SCREAMING_SNAKE_CASE       (__A							,       __A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : str      			=     CycleDiffusionPipeline
  _a  : int      			=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
      '''negative_prompt''',
      '''height''',
      '''width''',
      '''negative_prompt_embeds''',
  }
  _a  : str      			=     PipelineTesterMixin.required_optional_params - {'''latents'''}
  _a  : str      			=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}       )
  _a  : Optional[Any]      			=     IMAGE_TO_IMAGE_IMAGE_PARAMS
  _a  : Optional[int]      			=     IMAGE_TO_IMAGE_IMAGE_PARAMS







  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			UNetaDConditionModel(
             block_out_channels=(32, 64)   ,	layers_per_block=2   ,	sample_size=32   ,	in_channels=4   ,	out_channels=4   ,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,	cross_attention_dim=32   ,	)
         a_						     =			DDIMScheduler(
             beta_start=0.00_085   ,	beta_end=0.012   ,	beta_schedule='scaled_linear'   ,	num_train_timesteps=1_000   ,	clip_sample=UpperCamelCase__   ,	set_alpha_to_one=UpperCamelCase__   ,	)
         torch.manual_seed(0	)
         a_						     =			AutoencoderKL(
             block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,	latent_channels=4   ,	)
         torch.manual_seed(0	)
         a_						     =			CLIPTextConfig(
             bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=32   ,	intermediate_size=37   ,	layer_norm_eps=1e-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1_000   ,	)
         a_						     =			CLIPTextModel(UpperCamelCase__	)
         a_						     =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

         a_						     =			{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         return components







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__=0	):






         """simple docstring"""


         a_						     =			floats_tensor((1, 3, 32, 32)   ,	rng=random.Random(UpperCamelCase__	)	).to(UpperCamelCase__	)
         a_						     =			image / 2 + 0.5
         if str(UpperCamelCase__	).startswith('mps'	):
                a_						     =			torch.manual_seed(UpperCamelCase__	)
         else:
                a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(UpperCamelCase__	)
         a_						     =			{
             'prompt': 'An astronaut riding an elephant',
             'source_prompt': 'An astronaut riding a horse',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'eta': 0.1,
             'strength': 0.8,
             'guidance_scale': 3,
             'source_guidance_scale': 1,
             'output_type': 'numpy',
         }
         return inputs







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			'cpu'  # ensure determinism for the device-dependent torch.Generator

         a_						     =			self.get_dummy_components()
         a_						     =			CycleDiffusionPipeline(**UpperCamelCase__	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			self.get_dummy_inputs(UpperCamelCase__	)
         a_						     =			pipe(**UpperCamelCase__	)
         a_						     =			output.images

         a_						     =			images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         a_						     =			np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2







  @unittest.skipIf(torch_device != 'cuda'   ,	'This test requires a GPU'	)
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.get_dummy_components()
         for name, module in components.items():
                if hasattr(UpperCamelCase__   ,	'half'	):
                       a_						     =			module.half()
         a_						     =			CycleDiffusionPipeline(**UpperCamelCase__	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			self.get_dummy_inputs(UpperCamelCase__	)
         a_						     =			pipe(**UpperCamelCase__	)
         a_						     =			output.images

         a_						     =			images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         a_						     =			np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_save_load_local()







  @unittest.skip('non-deterministic pipeline'	)
  def    _a       (			self	):






         """simple docstring"""


         return super().test_inference_batch_single_identical()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_dict_tuple_outputs_equivalent()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_save_load_optional_components()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_attention_slicing_forward_pass()





@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/cycle-diffusion/black_colored_car.png'	)
         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'	)
         a_						     =			init_image.resize((512, 512)	)

         a_						     =			'CompVis/stable-diffusion-v1-4'
         a_						     =			DDIMScheduler.from_pretrained(UpperCamelCase__   ,	subfolder='scheduler'	)
         a_						     =			CycleDiffusionPipeline.from_pretrained(
             UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	safety_checker=UpperCamelCase__   ,	torch_dtype=torch.floataa   ,	revision='fp16'	)

         pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)
         pipe.enable_attention_slicing()

         a_						     =			'A black colored car'
         a_						     =			'A blue colored car'

         a_						     =			torch.manual_seed(0	)
         a_						     =			pipe(
             prompt=UpperCamelCase__   ,	source_prompt=UpperCamelCase__   ,	image=UpperCamelCase__   ,	num_inference_steps=100   ,	eta=0.1   ,	strength=0.85   ,	guidance_scale=3   ,	source_guidance_scale=1   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)
         a_						     =			output.images

         # the values aren't exactly equal, but the images look the same visually
         assert np.abs(image - expected_image	).max() < 5e-1







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/cycle-diffusion/black_colored_car.png'	)
         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'	)
         a_						     =			init_image.resize((512, 512)	)

         a_						     =			'CompVis/stable-diffusion-v1-4'
         a_						     =			DDIMScheduler.from_pretrained(UpperCamelCase__   ,	subfolder='scheduler'	)
         a_						     =			CycleDiffusionPipeline.from_pretrained(UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	safety_checker=UpperCamelCase__	)

         pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)
         pipe.enable_attention_slicing()

         a_						     =			'A black colored car'
         a_						     =			'A blue colored car'

         a_						     =			torch.manual_seed(0	)
         a_						     =			pipe(
             prompt=UpperCamelCase__   ,	source_prompt=UpperCamelCase__   ,	image=UpperCamelCase__   ,	num_inference_steps=100   ,	eta=0.1   ,	strength=0.85   ,	guidance_scale=3   ,	source_guidance_scale=1   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)
         a_						     =			output.images

         assert np.abs(image - expected_image	).max() < 2e-2




